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Open Access Research article

An exact approach for studying cargo transport by an ensemble of molecular motors

Donatello Materassi1*, Subhrajit Roychowdhury2, Thomas Hays3 and Murti Salapaka2

Author Affiliations

1 Laboratory for Information and Decision Systems, Massachussets Institute of Technology, 77 Massachusetts Avenue Cambridge, MA 02139, USA

2 Department of Electrical and Computer Engineering, University of Minnesota 200 Union Street SE, Minneapolis, MN 55455, USA

3 Department of Genetics, Cell Biology, and Development, University of Minnesota, 321 Church Street SE, Minneapolis, MN 55455, USA

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BMC Biophysics 2013, 6:14  doi:10.1186/2046-1682-6-14

Published: 16 November 2013



Intracellular transport is crucial for many cellular processes where a large fraction of the cargo is transferred by motor-proteins over a network of microtubules. Malfunctions in the transport mechanism underlie a number of medical maladies.

Existing methods for studying how motor-proteins coordinate the transfer of a shared cargo over a microtubule are either analytical or are based on Monte-Carlo simulations. Approaches that yield analytical results, while providing unique insights into transport mechanism, make simplifying assumptions, where a detailed characterization of important transport modalities is difficult to reach. On the other hand, Monte-Carlo based simulations can incorporate detailed characteristics of the transport mechanism; however, the quality of the results depend on the number and quality of simulation runs used in arriving at results. Here, for example, it is difficult to simulate and study rare-events that can trigger abnormalities in transport.


In this article, a semi-analytical methodology that determines the probability distribution function of motor-protein behavior in an exact manner is developed. The method utilizes a finite-dimensional projection of the underlying infinite-dimensional Markov model, which retains the Markov property, and enables the detailed and exact determination of motor configurations, from which meaningful inferences on transport characteristics of the original model can be derived.


Under this novel probabilistic approach new insights about the mechanisms of action of these proteins are found, suggesting hypothesis about their behavior and driving the design and realization of new experiments.

The advantages provided in accuracy and efficiency make it possible to detect rare events in the motor protein dynamics, that could otherwise pass undetected using standard simulation methods. In this respect, the model has allowed to provide a possible explanation for possible mechanisms under which motor proteins could coordinate their motion.

Molecular motors; Rare event detection; Markov models